Field of the Disclosure
The present disclosure relates to content delivery over a distributed communication network, and more particularly, to managing content delivery over content delivery networks (CDNs).
Description of the Related Art
The Internet of today is an ever-changing environment, including for example, a variety of distributed systems of servers deployed in multiple data centers around the world (e.g., content delivery networks (CDNs)). Content providers use networks such as CDNs to meet ever increasing demand for digital content over communication networks. For example, CDNs can locally store and serve requested content to end users more efficiently than origin servers storing the same content.
In an effort to better manage, distribute, operate, and implement CDNs, CDN providers often collect and aggregate telemetry data from various network nodes and/or devices. However, the scale and complexity of CDNs often results in correspondingly a large and complex amount of telemetry data. Further, the amount of telemetry data is multiplied when a CDN network maintains a persistent, bidirectional connection to corresponding client devices since telemetry data is generated both “to” and “from” client device. Moreover, additional challenges arise when telemetry data is generated by different types of client devices, since each client device may generate a different type or format of telemetry data. Further complicating telemetry data collection is the inherent nature of a CDN environment which dynamically serves content, in real-time, to a constantly changing pool of requesting client devices.
Conventional telemetry data systems employed by content providers often include a separate architecture of devices, independent from underlying application servers that facilitate transfer of the content/data within a network. Such conventional telemetry data systems prove expensive, complex, and require a large amounts of computing resources to handle many chunks of telemetry data, configure heterogeneous telemetry data, and translate telemetry data collected by devices having different operating systems. Operatively, conventional telemetry data systems may manage telemetry data by collecting and parsing logs of telemetry data, which may require several parsing steps to transform telemetry data into a human-readable form. Further, some parsing steps can include recording a measurement for each tracked variable from a beginning of the log or from a last measurement and comparing a current measurement against the recorded measurement to determine a delta or changed state. Typically, the entire log, including recorded measurements, and changed states are stored for verification and later comparisons. Accordingly, real-time data management by conventional telemetry data systems often proves difficult, at best.